Which tool, for which job

The real choice isn't speed vs accuracy, it's whether you have 32 GB of RAM and clean RNA, or 8 GB and degraded FFPE samples.

One assay finds every open door in the genome, the other tells you who's standing in a specific doorway, the question you're asking decides which one you run.

Cell number, background level, and peak-caller defaults diverge enough that swapping one assay for the other without retuning your pipeline hands you peaks that are not real.

Bulk ATAC-seq gives you cleaner peaks and honest statistics; scATAC-seq trades that precision for cell-type resolution you can't get any other way.

Frozen tissue and unbiased cell-type capture on one side, fuller transcriptomes and dissociation bias on the other, and the QC thresholds do not transfer between them.

The matrices are transposed relative to each other, and that mismatch is where most conversion bugs and silent data loss actually start.

Both objects hold the same counts, metadata and embeddings, the real choice is which ecosystem of functions you want opening the box.

Same negative-binomial model family, different normalization and dispersion philosophy, and that difference decides which one protects you when replicate numbers are small.

Counts with a GLM or log-CPM with precision weights: the right pick flips once your sample size crosses from a handful of replicates into the hundreds.

Same lab, same Bioconductor ecosystem, but a negative-binomial GLM and a voom-transformed linear model bite in different places once your design gets complicated or your cohort gets big.

Both embeddings will lie to you about distance and cluster size; the real question is which one lies faster and more consistently.

One corrects the map, the other rewrites the territory, knowing which matters before you run your first FindMarkers call.

Both are the conservative choice in their family, but one corrects coordinates and the other builds a new assay, and that difference decides which downstream code you can run.

Both run through the same FindIntegrationAnchors framework, but they disagree on how willing they are to call two cells from different batches "the same cell."

Selective alignment with GC bias correction against pure pseudoalignment: near-identical answers on clean data, real divergence on biased libraries, and only one ships sleuth-ready bootstraps.

Same BAM, same GTF, but a tenfold-plus speed gap and different defaults for overlapping and multi-mapped reads mean the two tools rarely produce identical count matrices.

The real fight isn't Seurat vs DESeq2, it's cells vs samples as your unit of replication, and getting that wrong is how you publish a false positive.

Feature parity is high, so the real decision comes down to which language the rest of your team already lives in and how many cells you're about to throw at it.

Both cluster spatial spots, but only one of them can tell you whether two cell types actually sit next to each other more than chance.

Python rules versus a dataflow DSL: what actually changes when you pick one over the other for a real pipeline.